Smooth Nonlinear Optimization in Rn

  • Tamás Rapcsák

Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 19)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Tamás Rapcsák
    Pages 1-6
  3. Tamás Rapcsák
    Pages 7-25
  4. Tamás Rapcsák
    Pages 27-36
  5. Tamás Rapcsák
    Pages 37-44
  6. Tamás Rapcsák
    Pages 61-86
  7. Tamás Rapcsák
    Pages 111-139
  8. Tamás Rapcsák
    Pages 141-166
  9. Tamás Rapcsák
    Pages 167-183
  10. Tamás Rapcsák
    Pages 185-206
  11. Tamás Rapcsák
    Pages 231-251
  12. Tamás Rapcsák
    Pages 253-270
  13. Back Matter
    Pages 285-375

About this book


Experience gained during a ten-year long involvement in modelling, program­ ming and application in nonlinear optimization helped me to arrive at the conclusion that in the interest of having successful applications and efficient software production, knowing the structure of the problem to be solved is in­ dispensable. This is the reason why I have chosen the field in question as the sphere of my research. Since in applications, mainly from among the nonconvex optimization models, the differentiable ones proved to be the most efficient in modelling, especially in solving them with computers, I started to deal with the structure of smooth optimization problems. The book, which is a result of more than a decade of research, can be equally useful for researchers and stu­ dents showing interest in the domain, since the elementary notions necessary for understanding the book constitute a part of the university curriculum. I in­ tended dealing with the key questions of optimization theory, which endeavour, obviously, cannot bear all the marks of completeness. What I consider the most crucial point is the uniform, differential geometric treatment of various questions, which provides the reader with opportunities for learning the structure in the wide range, within optimization problems. I am grateful to my family for affording me tranquil, productive circumstances. I express my gratitude to F.


Optimality Conditions Optimization algorithm Optimization algorithms algorithms linear optimization nonlinear optimization optimization sets

Authors and affiliations

  • Tamás Rapcsák
    • 1
  1. 1.Computer and Automation Institute of Hungarian Academy of SciencesBudapestHungary

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag US 1997
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-7920-1
  • Online ISBN 978-1-4615-6357-0
  • Series Print ISSN 1571-568X
  • Buy this book on publisher's site